MGDE: a many-objective guided differential evolution with strengthened dominance relation and bi-goal evolution

Djaafar Zouache () and Fouad Ben Abdelaziz ()
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Djaafar Zouache: University of Mohamed El Bachir El Ibrahimi
Fouad Ben Abdelaziz: NEOMA Business School

Annals of Operations Research, 2025, vol. 346, issue 2, No 45, 1929-1966

Abstract: Abstract When solving many-objective optimization problems, evolutionary algorithms do not differentiate between solutions due to the large number of objectives involved, and therefore they do not generate an accurate potential efficient set. The main problem such algorithms encounter is that of the balance between convergence and diversity. To overcome this difficulty, we propose a Guided Differential Evolution method (MGDE) that uses strengthened dominance relation and bi-goal evolution to solve many-objective optimization problems (MaOPs). We also use adapted differential evolutionary operators for the crossover and mutation to guide the search. This guided exploration strategy makes it possible to converge toward the Pareto front with good solution diversity. We use the CEC 2017 Competition problems to test our algorithm and show that it performs better than most recent algorithms.

Keywords: Many-objective optimization; Differential evolution; Strengthened dominance relation; Bi-goal evolution (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-022-04641-3

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